Multi-omics integration with MOFA2

Published

30 Jan 2026

Overview

This report presents a structured MOFA2 analysis integrating transcriptomics, metagenomics, and metabolomics data. The goal is to identify latent factors capturing shared and view-specific sources of variation, and to generate a set of publication-ready figures that can be combined into a multi‑panel figure.

Main outcomes: - Latent factor structure across multi‑omics layers - Variance explained per factor and per view - Associations between factors and clinical / technical covariates - Feature loadings driving key factors

Analysis workflow (step‑by‑step)

Load required libraries and define plotting palettes
Load omics data and sample metadata
Format MOFA input
Select samples for inclusion
Unsupervised UMAPs
Configure and run the MOFA model
Load trained MOFA model
Perform model diagnostics and quality checks
Covariates- Factor correlation
Annotate latent factors using group information
Feature-level figures
Unified UMAP (all views)

1. Libraries and global settings

Code

library(MOFA2)
library(tidyverse)
library(ggpubr)
library(GGally)
library(psych)
library(pheatmap)
library(cowplot)
library(RColorBrewer)
library(readxl)
library(ComplexHeatmap)
library(circlize)
library(patchwork)

# setwd("/Users/vilkal/work/Brolidens_work/Projects/Candida-omics/src")

Define consistent colour palettes to ensure visual coherence across all figures.

Code

select <- dplyr::select
map <- purrr::map
rename <- dplyr::rename

pal <- list(
  subCST = c(
    "#a5c97b",
    "#cc93cf",
    "#de9548",
    "#63d3b4",
    "#9e8dec",
    "#902267",
    '#FB5273',
    "#91c5de"
  ),
  RVVC = c("#EAEAEA", "#fd814e"),
  BV = c("#EAEAEA", "#fd814e"),
  NAC = c("#EAEAEA", "#fd814e"),
  ST = c("#EAEAEA", "#fd814e"),
  missing_RVVC = c("#EAEAEA", "#fd814e"),
  Clin = c("#a5c97b", "#cc93cf", "#de9548", "#63d3b4", "#9e8dec", "#902267"),
  Clin_gr = c("#ffa998", '#FB5273', "#902267"),
  RVVC_AS = c("#ffa998", '#FB5273', "#902267"),
  RVVC_pos = c(
    "#A6CEE3",
    "#1F78B4",
    #"#EAEAEA",
    #"gray",
    "#e490c4ff",
    "#c12d88ff"
  ), # "#FB9A99", "#E31A1C", "#B2DF8A", "#609c5bff", "#FDBF6F", "#FF7F00",, "#CAB2D6", "#6a3d9a" "#e490c4ff", "#c12d88ff"
  trx_louvain_7 = c(
    "#de9548",
    "#cc93cf",
    "#a5c97b",
    "#9e8dec",
    "#63d3b4",
    "#902267",
    '#FB5273'
  ),
  metab_louvain = c(
    "#a5c97b",
    "#cc93cf",
    "#de9548",
    "#63d3b4",
    "#9e8dec",
    "#902267",
    '#FB5273'
  ),
  microb_louvain = c(
    "#de9548",
    "#cc93cf",
    "#a5c97b",
    "#9e8dec",
    "#902267",
    "#63d3b4"
  ),
  integ_louvain = c(
    "#a5c97b",
    "#cc93cf",
    "#de9548",
    "#63d3b4",
    "#9e8dec",
    "#902267",
    '#FB5273',
    "#984EA3"
    #"#B3CDE3"
  ),
  "Wet_smear:_clue_cells_(y/n)" = c("#2266ac", "#91c5de"),
  hyphae = c("#f26386", "#fd814e", "#a4d984", "#fbbc52"),
  pos = c("#91c5de", "#2266ac"),
  "Clinical_score_(0-5)" = brewer.pal(6, name = "Reds")
)
gr_names <- c("Control", "RVVC", "RVVC_H")

2. Data loading

Multi‑omics matrices and sample metadata are loaded from pre‑processed RDS/CSV files. These data have already undergone quality control and normalisation upstream.

Clin_path <- "/Users/vilkal/work/Brolidens_work/Projects/Candida-omics/data/Metadata_svamp_FINAL.xlsx"
comb_meta_path <- "../Results/Combined_gr_meta_data.csv"

# meta data
Clin_data <- read_xlsx(Clin_path, sheet = "Metadata", na = "na")
meta <- read_csv(comb_meta_path) %>%
  rename(sample = "svamp_ID") %>%
  mutate(across(2:25, ~ factor(.x)))
top_hvg <- read_csv("../Results/Tissue_QC/HVG.csv")


TRX_path <- "../Results/MixOmic/Transcriptomics_batch_filt.csv"
MG_path <- "../Results/MixOmic/Metageonome_vagswab_CLR_transformed.RDS"
MB_path <- "../Results/MixOmic/Metabolom_Normalized.csv"

# expression data
TRX_batch <- read.csv(TRX_path, row.names = 1)
MG_matrix <- readRDS(MG_path)
MB_matrix <- read.csv(MB_path, row.names = 1)

3. Prepare MOFA input

4.1 Assemble omics views

MOFA describes each omic dataset as a “view”. One of the possible inputs to mofa is a list of matrixes for each view. Each omics layer is formatted as a features × samples matrix. Sample ordering needs to be the same. The top 5000 most higly variable genes identified in the QC_Transcriptomics.qmd script is used to filter the transcriptomics data.

# remove long strain id, replace with int
MG_matrix <- MG_matrix[["clr_data"]][["strain"]]
new_names <- sub("\\s+\\S+$", "", colnames(MG_matrix)) # remove strain id
new_names <- make.unique(new_names, sep = " ") # Make names unique
colnames(MG_matrix) <- new_names # Assign new names

# select 5000 most higly variable genes
TRX_HVG <- TRX_batch[top_hvg$gene[1:5000], ]

views <- list(
  Transcriptomics = as.matrix(TRX_HVG),
  Metagenomics = unclass(t(MG_matrix)),
  Metabolomics = as.matrix(MB_matrix)
)

4.2 Convert matrices to long format

The matrix list input is only valid if you have matching samples for all “views”. If you wish to include samples that are not “complete” in this sense the long format version is another input alternative to create a MOFA model

omics_to_long <- function(mat, view_name) {
  mat %>%
    as.data.frame() %>%
    rownames_to_column("feature") %>%
    pivot_longer(
      cols = -feature,
      names_to = "sample",
      values_to = "value"
    ) %>%
    mutate(view = view_name)
}

mofa_df <- imap_dfr(views, ~ omics_to_long(.x, .y))

4.3 Sample criteria df

The tibble called m shows what samples excist across views for each study participant

# select samples to include
m <- mofa_df %>%
  distinct(sample, view) %>%
  mutate(present = TRUE) %>%
  pivot_wider(
    id_cols = sample,
    names_from = view,
    values_from = present,
    values_fill = FALSE
  )

m <- m %>%
  mutate(
    n_views = rowSums(across(-sample)),
    at_least_two = n_views >= 2,
    all_BL = !(grepl("_3M|_1W|_6M", .$sample)),
    matched = n_views == 3
  )

write_csv(m, "../Results/Sample_omic_table.csv")
# m <- write_csv("../Results/Sample_omic_table.csv")

knitr::kable(head(m))

sample	Transcriptomics	Metagenomics	Metabolomics	n_views	at_least_two	all_BL	matched
S01	TRUE	TRUE	TRUE	3	TRUE	TRUE	TRUE
S02	TRUE	TRUE	TRUE	3	TRUE	TRUE	TRUE
S03	TRUE	TRUE	TRUE	3	TRUE	TRUE	TRUE
S04	TRUE	TRUE	TRUE	3	TRUE	TRUE	TRUE
S05	TRUE	TRUE	TRUE	3	TRUE	TRUE	TRUE
S06	TRUE	TRUE	TRUE	3	TRUE	TRUE	TRUE

Based on the various criteria we filter out the samples we do not wish to include from our long format table

mofa_df <- mofa_df %>%
  filter(sample %in% m$sample[m$at_least_two & m$all_BL])

meta_ <- meta %>% filter(sample %in% m$sample[m$at_least_two & m$all_BL])

4. Sample heatmap overview

With our samples selected we can now plot a overviw heatmap showing all our included samples, which omic views they are represented in and how they are distributed by some of our groups of intrest

Code

heatmap_prepp <- function(keep_s, vars, arrange_by = "Clinical_score_(0-5)") {
  ### ------------------------------------------------------------
  ### 1. Select symptom columns and prepare binary clinical data
  ### ------------------------------------------------------------

  # Extract only samples of interest and symptom score columns
  # Convert TRUE/FALSE columns to numeric 0/1 and clean column names
  df_bin <- m %>%
    #filter(sample %in% m$sample[m$at_least_two & m$all_BL]) %>%
    filter(sample %in% m$sample[keep_s]) %>%

    left_join(
      .,
      select(meta, sample, RVVC_AS, RVVC_pos, "Clinical_score_(0-5)"),
      by = "sample"
    ) %>%
    mutate(
      RVVC_AS = factor(RVVC_AS, levels = c("RVVC", "Control", "RVVC_AS"))
    ) %>%
    mutate(
      RVVC_pos = factor(
        RVVC_pos,
        levels = c(
          "RVVC_pos",
          "RVVC_neg",
          "Control_pos",
          "Control_neg"
          #"NA_pos",
          #"NA_neg"
        )
      )
    ) %>%
    arrange(desc(n_views)) %>%
    arrange(RVVC_pos) %>%
    arrange(desc(`Clinical_score_(0-5)`)) %>%
    arrange(
      desc(.data[[arrange_by]])
    ) %>%
    select(1:4) %>%
    mutate(across(where(is.logical), as.integer)) %>%
    column_to_rownames("sample") # convert sample ID to rownames

  # Convert to numeric matrix for ComplexHeatmap
  mat <- as.matrix(df_bin)

  # Determine row annotation height scaling factor
  my_h <<- 0.1 * nrow(mat)

  ### ------------------------------------------------------------
  ### 2. Prepare annotation dataframe (metadata)
  ### ------------------------------------------------------------

  annot_col <- meta %>%
    filter(sample %in% rownames(df_bin)) %>% # keep same samples as heatmap matrix
    arrange(match(sample, rownames(df_bin))) %>%
    select(sample, any_of(vars)) %>% # select annotation variables
    mutate(across(all_of(vars), ~ factor(.x))) %>%
    #mutate(across(1:length(vars) + 1, ~ factor(.x))) %>% # ensure annotations are factors
    column_to_rownames("sample") # convert sample ID to rownames

  # Quick check: ensure annotation rows match heatmap rows
  dim(mat)
  dim(annot_col)

  setdiff(rownames(mat), rownames(annot_col))

  ### ------------------------------------------------------------
  ### 4. Define row annotations for heatmap (ComplexHeatmap)
  ### ------------------------------------------------------------

  annot <- HeatmapAnnotation(
    df = annot_col[, c(vars)],
    which = c("row"),
    #which = c("column"),
    # Clin_gr = annot_col$Clin_gr, # binned clinical grade
    # "Clinical_score_(0-5)" = annot_col$`Clinical_score_(0-5)`, # raw clinical score
    # RVVC_AS = annot_col$RVVC_AS, # sample group
    # BV = annot_col$BV, # sample group
    # RVVC = annot_col$RVVC, # sample group
    # hyphae = annot_col$hyphae, # sample group

    show_annotation_name = TRUE, # show labels
    annotation_name_gp = gpar(fontsize = 8), # label font size

    # Legend formatting
    annotation_legend_param = list(
      grid_height = unit(.1, "mm"),
      grid_width = unit(2, "mm"),
      title = "",
      labels_gp = gpar(fontsize = 7),
      title_gp = gpar(fontsize = 8)
    ),

    simple_anno_size = unit(.3, "cm"), # annotation row height

    # Colors for each annotation variable
    na_col = "white",
    col = pal[vars] %>% imap(., ~ set_names(.x, levels(annot_col[[.y]])))
  )
  ### ------------------------------------------------------------
  ### Create heatmap
  ### ------------------------------------------------------------
  H <- Heatmap(
    mat,
    width = unit(ncol(mat) * 5, "mm"),
    height = unit(nrow(mat) * 2.2, "mm"),
    name = "Symptom Present",
    col = col_fun,

    # vertical orientation:
    right_annotation = annot,
    cluster_rows = FALSE,
    row_order = rownames(mat),

    row_names_side = "right",

    column_names_rot = 90,
    row_names_gp = gpar(fontsize = 7),
    heatmap_legend_param = list(
      at = c(0, 1),
      labels = c("No", "Yes")
    ),
    #   cell_fun = function(j, i, x, y, width, height, fill) {
    #   grid.rect(x, y, width, height, gp = gpar(col = "grey70", lwd = 0.5, fill = NA))
    # }
    layer_fun = function(j, i, x, y, width, height, fill) {
      # Draw cell borders without overwriting fill
      grid.rect(
        x = x,
        y = y,
        width = width,
        height = height,
        gp = gpar(col = "grey70", lwd = 0.4, fill = NA)
      )
    }
  )
  #return(list(mat = mat, annot = annot, h = my_h))
  return(H)
}

Code

### ------------------------------------------------------------
### Color function for the symptom heatmap
### ------------------------------------------------------------
col_fun <- colorRamp2(
  c(0, 0.6, 1),
  c("white", "white", "#e05e85")
)

### ------------------------------------------------------------
### Prepare annotation df and matrix
### ------------------------------------------------------------

vars <- c(
  "Clinical_score_(0-5)",
  "Clin_gr",
  "RVVC",
  #"RVVC_AS",
  "RVVC_pos",
  "BV",
  "NAC",
  "missing_RVVC",
  "ST"
)

keep_s <- rep(TRUE, nrow(m)) # 152 every samples incl. follow up
keep_s <- m$all_BL # 91 samples
keep_s <- m$matched # 70 samples
keep_s <- m$at_least_two & m$all_BL # 89 samples

H <- heatmap_prepp(keep_s, vars)

H <- heatmap_prepp(
  keep_s,
  c("RVVC_pos", "RVVC_AS", "Clinical_score_(0-5)"),
  arrange_by = "RVVC_pos"
)

H <- heatmap_prepp(
  keep_s,
  c("pos", "RVVC_pos", "Clinical_score_(0-5)"),
  arrange_by = "RVVC_AS"
)
# quartz(width = 6, height = 6, dpi = 150)
# draw(H)

Code

# !expr my_h

# Heatmap global options:
# ht_opt$COLUMN_ANNO_PADDING = unit(.05, "cm")
# ht_opt$HEATMAP_LEGEND_PADDING = unit(-0, "cm") #unit(c(0, 0, 0, -1), "cm") #b,l,t,r
# ht_opt$TITLE_PADDING = unit(.05, "cm")
# ht_opt$DIMNAME_PADDING = unit(.05, "cm")

# ht_opt(LEGEND_GAP = unit(18, "mm"))

H_g <- draw(
  H,
  heatmap_legend_side = "bottom",
  merge_legend = F,
  background = NA,
  show_annotation_legend = FALSE,
  legend_gap = unit(5, "mm"),
  padding = unit(c(3, 9, -2, 3), "mm") # outer padding
) # b,l,t,r

Code

################
# SAVE RESULTS #
################
# quartz(width = 6, height = 6, dpi = 150)
png(
  file = paste0("../Results/Sample_HT/BL_89.png"),
  units = "in",
  res = 600,
  #width = 8.5, height = 10 # Epi
  width = 8.5,
  height = 11 # 11 # my_h # 17 (all samples)
)
H_g
#draw(H)
dev.off()

quartz_off_screen 
                2

5. Unsupervised UMAPs

We plot unsupervised umaps coloured by different groups of intrest in order to see how they cluster across views.

!NB should think about doing with and without HVGs only

# filter samples from views for UMAP ploting
m_ <- m %>%
  filter(sample %in% .$sample[m$at_least_two & m$all_BL])

v <- views %>%
  imap(., ~ .x[, m_$sample[m_[[.y]]]])

This first code is taken from Paulos analysis of the metabolomics data. It gives more control over the knn clustering by perfoming it in a seperate step and then supplying the knn to the umap function. The other alternative bellow is just supplying the data to umap, which then runs the PCA and knn internaly with default setings.

Code

#### UMAP analysis ####
set.seed(1)
UMAP_df <- v %>%
  map(., ~ t(.x)) %>%
  # PCA
  map(., ~ prcomp(.x, scale = T, center = T)) %>%
  # KNN graph
  map(
    .,
    ~ RcppHNSW::hnsw_knn(as.matrix(.x$x[, 1:10]), k = 10, distance = "cosine")
  ) %>%

  # UMAP
  map(
    .,
    ~ uwot::umap(
      X = NULL,
      nn_method = .x,
      n_components = 2,
      ret_extra = c("model", "fgraph"),
      verbose = T,
      min_dist = 0.1,
      spread = 0.3,
      approx_pow = TRUE,
      repulsion_strength = .2,
      negative_sample_rate = 30,
      n_epochs = 200,
      n_threads = 8
    )$embedding
  ) %>%
  imap(
    .,
    ~ tibble(
      "UMAP 1" = .x[, 1],
      "UMAP 2" = .x[, 2],
      "ID" = colnames(v[[.y]])
    )
  ) %>%
  map(., ~ left_join(., annot_col, by = c("ID" = "sample")))

Code

UMAP_fun <- function(
  umap_df,
  group_var,
  txt_var = NULL,
  shape_gr = NULL,
  shape = NULL,
  title = ""
) {
  if (is.null(shape_gr)) {
    point <- geom_point(size = 3, alpha = 1, shape = 21)
  } else {
    point <- geom_point(
      size = 3,
      alpha = 1,
      aes(shape = if (!is.null(shape_gr)) .data[[shape_gr]] else NULL)
    )
  }

  p <- ggplot(
    umap_df,
    aes(
      x = `UMAP 1`,
      y = `UMAP 2`,
      #shape = RVVC_AS,
      fill = .data[[group_var]],
      color = .data[[group_var]]
    )
  ) +
    point +
    #geom_jitter( shape=21, size=3, color="white", width=.5, height=.5) +  # Tassos used jitter

    scale_fill_manual(
      values = pal[[group_var]],
      na.value = "transparent",
      name = ""
      #aesthetics = c("colour", "fill")
    ) +
    guides(fill = "none") +
    scale_colour_manual(
      values = pal[[group_var]],
      na.value = "gray",
      name = ""
    ) +
    {
      if (!is.null(shape_gr) && !is.null(shape)) {
        scale_shape_manual(values = shape, name = "")
      }
    } +
    # scale_shape_manual(values = c(21, 5, 4), name = "" ) + #specify individual shapes+
    {
      if (!is.null(txt_var)) {
        geom_text(
          data = umap_df,
          aes(x = `UMAP 1`, y = `UMAP 2`, label = .data[[txt_var]]),
          size = 2,
          hjust = 0,
          nudge_x = 0.07,
          color = "gray51"
        )
      }
    } +
    ggtitle(title) +
    theme_bw(base_size = 14) +
    theme(
      # point = element_point(shape = 21),
      line = element_blank(),
      axis.ticks = element_line(color = "black"),
      aspect.ratio = 1,
      axis.title = element_blank()
    )
  return(p)
}


#### UMAP plotting by groups ####

# save plot to file
#ggsave(paste0("./Figures/09/", "Bulk_UMAP.png"), p)

Code

#### group information ####
annot_col <- meta %>%
  #filter(sample %in% .$sample[m$at_least_two & m$all_BL]) %>%
  # select(-`Age (years)`) %>%
  select(
    1:22,
    `Clinical_score_(0-5)`
  ) %>%
  mutate(across(2:ncol(.), ~ factor(.x))) %>%
  mutate(
    txt_clin = ifelse(
      grepl("0", .$`Clinical_score_(0-5)`),
      NA,
      as.character(.$`Clinical_score_(0-5)`)
    )
  ) %>%
  mutate(txt_id = ifelse(RVVC == "RVVC", .$sample, NA))

#### UMAP analysis ####
set.seed(1)

umap_dfs <- v %>%
  map(., ~ t(.x)) %>%
  map(
    .,
    ~ uwot::umap(
      .x,
      #seed = 123,
      seed = 463,
      n_neighbors = 15,
      #metric = "cosine",
      init = "spectral",
      scale = T
    )
  ) %>%
  map(
    .,
    ~ tibble(
      "UMAP 1" = .x[, 1],
      "UMAP 2" = .x[, 2],
      "ID" = rownames(.x)
    )
  ) %>%
  map(., ~ left_join(., annot_col, by = c("ID" = "sample")))

meta_clus <- colnames(annot_col)
color_vars <- c(
  "pos",
  "trx_louvain_7",
  "metab_louvain",
  "microb_louvain",
  "integ_louvain",
  "Clinical_score_(0-5)",
  "RVVC_pos",
  "RVVC_AS"
) %>%
  set_names(.)

#### Combine and save plots ####
make_umap_panel <- function(var, umap_dfs, outdir = "../Results/UMAP/") {
  # create plots for each dataset
  p_list <- imap(
    umap_dfs,
    ~ UMAP_fun(.x, var, "txt_clin", title = .y)
  )

  # combine with shared legend
  p_combined <- patchwork::wrap_plots(p_list) +
    patchwork::plot_layout(guides = "collect") &
    theme(legend.position = "right")

  # safe filename
  fname <- gsub("[^A-Za-z0-9_]", "_", var)

  # save
  ggsave(
    filename = paste0(outdir, "UMAP_", fname, ".png"),
    plot = p_combined,
    width = 14,
    height = 5,
    dpi = 300
  )

  return(p_combined)
}

plots <- map(color_vars, ~ make_umap_panel(.x, umap_dfs))
plots

$pos


$trx_louvain_7


$metab_louvain


$microb_louvain


$integ_louvain


$`Clinical_score_(0-5)`


$RVVC_pos


$RVVC_AS

Code

# use shapes:
# UMAP_fun(umap_dfs$Metabolomics, "trx_louvain_7", "txt_clin", shape_gr = "pos", shape = c(21, 24))
# no shapes
# UMAP_fun(umap_dfs$Metabolomics, "trx_louvain_7", "BV")

6. MOFA model construction

A MOFA object is created and configured with default data and training options. The number of latent factors is set a priori and can be tuned depending on variance explained.

Data options

Important arguments

scale_groups
Scale groups to the same total variance?
Default: FALSE
scale_views
Scale views to the same total variance?
Default: FALSE
views
Names of the views to include in the model.
groups
Names of the sample groups.

Model options

Important arguments

num_factors
Number of latent factors.
likelihoods
Likelihood per view.
Options: "gaussian", "poisson", "bernoulli"
By default, likelihoods are inferred automatically.
spikeslab_factors
Use spike-and-slab sparsity prior on the factors?
Default: FALSE
spikeslab_weights
Use spike-and-slab sparsity prior on the weights?
Default: TRUE
ard_factors
Use Automatic Relevance Determination (ARD) prior on the factors?
Default: TRUE when using multiple groups.
ard_weights
Use Automatic Relevance Determination (ARD) prior on the weights?
Default: TRUE when using multiple views.

Training options

Important arguments

maxiter
Maximum number of training iterations.
Default: 1000
convergence_mode
Convergence speed of the algorithm.
Options: "fast", "medium" (default), "slow"
For exploratory analyses, "fast" is usually sufficient.
seed
Random seed for reproducibility.

We use default options, however it is recomended to run the model several times with different seeds. In one of the papers published by MOFAS authors, they ran the model ~24 times, before picking the model with the highest elbo score.

MOFAobject <- create_mofa_from_df(mofa_df)

data_opts <- get_default_data_options(MOFAobject)
model_opts <- get_default_model_options(MOFAobject)
train_opts <- get_default_training_options(MOFAobject)

#model_opts$num_factors <- 10
train_opts$convergence_mode <- "medium"
train_opts$seed <- 1234

The functions bellow runs the model with different seeds and saves the model and a table with all seeds and corresponding elbo scores as output

Train MOFA model

seeds <- c(3, 100, 50)

run_mofa_with_seed <- function(seed) {
  # Make a local copy (important!)
  train_opts$seed <- seed

  MOFAobject <- prepare_mofa(
    MOFAobject,
    data_options = data_opts,
    model_options = model_opts,
    training_options = train_opts
  )

  MOFAmodel <- run_mofa(
    MOFAobject,
    outfile = paste0("../Results/MOFA/MOFA_allBL_seed_", seed, ".hdf5"),
    use_basilisk = TRUE
  )

  elbo <- max(get_elbo(MOFAmodel))

  # Return a single-row data frame
  tibble(
    seed = seed,
    elbo = elbo
  )
}

# existing training code stays the same
elbo_table <- map_dfr(seeds, ~ run_mofa_with_seed(.x))
write_csv(elbo_table, "../Results/MOFA/elbo_table.csv")
best_seed <- elbo_table$seed[which.max(elbo_table$elbo)]

MOFAobject without transcriptomics batch correction

X <- readRDS("../Results/MixOmic/X_object.RDS")
views <- list(
  Transcriptomics = t(X$strain$TR),
  Metagenomics = t(X$strain$MG),
  Metabolomics = t(X$species$MB)
)

MOFAobject <- create_mofa(views)

data_opts <- get_default_data_options(MOFAobject)
model_opts <- get_default_model_options(MOFAobject)
model_opts$num_factors <- 10

train_opts <- get_default_training_options(MOFAobject)
train_opts$convergence_mode <- "medium"
train_opts$seed <- 1234

MOFAobject <- prepare_mofa(
  MOFAobject,
  data_options = data_opts,
  model_options = model_opts,
  training_options = train_opts
)
MOFAmodel <- run_mofa(
  MOFAobject,
  outfile = "../Results/MOFA/MOFA_noBatch_strain.hdf5",
  use_basilisk = TRUE
)

7. Load trained MOFA model

We load the model with the highest elbo score

elbo_table <- read_csv("../Results/MOFA/elbo_table.csv")
knitr::kable(elbo_table, )

seed	elbo
3	-202997.0
100	-202991.6
50	-202995.3

best_seed <- elbo_table$seed[which.max(abs(elbo_table$elbo))]

MOFAmodel <- load_model(
  #"../Results/MOFA/MOFA_matched_seed_50.hdf5"
  paste0("../Results/MOFA/MOFA_allBL_seed_", best_seed, ".hdf5")
)

# existing training code stays the same
samples_metadata(MOFAmodel) <- samples_metadata(MOFAmodel)[, c(
  "sample",
  "group"
)] %>%
  left_join(meta, by = "sample")

# batch corrected tr, ~17 000 genes, strain lvl, no followup samples
"../Results/MOFA/MOFA_matched_noM3_strain.hdf5"

[1] "../Results/MOFA/MOFA_matched_noM3_strain.hdf5"

# batch corrected tr, ~17 000 genes, strain lvl + 7 M3 follow up samples
"../Results/MOFA/MOFA_w73M_strain.hdf5"

[1] "../Results/MOFA/MOFA_w73M_strain.hdf5"

# same as above + 9 extra RVVC samples without matching trx
"../Results/MOFA/MOFA_w9RVVC_strain.hdf5"

[1] "../Results/MOFA/MOFA_w9RVVC_strain.hdf5"

# same as above + all 154 samples
"../Results/MOFA/MOFA_all_154_samples_strain.hdf5"

[1] "../Results/MOFA/MOFA_all_154_samples_strain.hdf5"

8. Model diagnostics

# inspect data distribution
ggdensity(mofa_df, x = "value", fill = "gray70") +
  facet_wrap(~view, nrow = 3, scales = "free") # + xlim(0.5,)

We first assess the overall quality of the model by inspecting variance explained and factor correlations.

# inspect the model:
plot_data_overview(MOFAmodel)

MOFAmodel

Trained MOFA with the following characteristics: 
 Number of views: 3 
 Views names: Metabolomics Metagenomics Transcriptomics 
 Number of features (per view): 90 244 5000 
 Number of groups: 1 
 Groups names: single_group 
 Number of samples (per group): 89 
 Number of factors: 15

p1 <- plot_variance_explained(MOFAmodel, plot_total = TRUE)[[2]]
p2 <- plot_variance_explained(MOFAmodel, factors = 1:10) #+

#+
#theme(axis.text.x = element_text(angle = 45, hjust = 0.8, vjust = 0.9))
# plot_grid(plotlist = list(p1, p2), ncol = 2)

p <- plot_data_overview(MOFAmodel) +
  theme(plot.margin = unit(c(1, 0, 1, 1), "cm"))
p1 <- p1 +
  coord_flip() +
  theme_nothing() +
  theme(plot.margin = unit(c(1, 0.1, 1, 0), "cm"))
p2 <- p2 +
  coord_flip() +
  theme(axis.text.y = element_blank(), legend.position = "none") +
  theme(plot.margin = unit(c(1, 0.5, 1, 0), "cm")) +
  scale_y_discrete(labels = 1:10)

# quartz(width = 9.5, height = 2.7)
plot_grid(plotlist = list(p, p2, p1), ncol = 3, align = c("h"))

# p$layers[[1]]$aes_params$alpha

Correlation between factors

A good sanity check is to verify that the Factors are largely uncorrelated. In MOFA there are no orthogonality constraints such as in Principal Component Analysis, but if there is a lot of correlation between Factors this suggests a poor model fit. Reasons could be that you used too many factors or perhaps the normalisation is not adequate.

plot_factor_cor(MOFAmodel)

MOFAbatch <- load_model(
  "../Results/MOFA/MOFA_noBatch_strain.hdf5"
)

Warning in .quality_control(object, verbose = verbose): Factor(s) 1 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.

samples_metadata(MOFAbatch) <- samples_metadata(MOFAbatch) %>%
  left_join(meta, by = "sample")

plot_factors(
  MOFAbatch,
  factors = "all",
  color_by = "batch"
)

When running MOFA the first time, the internal quality control warned that at least one of the factors was strongly corelated with the total number of expressed features in one of the views. This is typically a result of batch effect. As seen in the plot above Factor 1 perfectly seperates the two batches of the transcriptomics data. Thus batch correction was performe in the upstream QC_Transcriptomics.qmd script.

p <- plot_factors(
  MOFAmodel,
  factors = "all",
  color_by = "Clinical_score_(0-5)"
) +
  scale_fill_manual(values = pal$`Clinical_score_(0-5)`, na.value = "white")

p

plot_factors(
  MOFAmodel,
  factors = c(1, 3),
  color_by = "Clin_gr"
) +
  stat_ellipse(
    #aes(color = RVVC_AS, fill = RVVC_AS),
    geom = "polygon",
    alpha = 0.25
  ) +
  scale_fill_manual(values = pal$Clin_gr, na.value = "white")

Too few points to calculate an ellipse

# quartz(width = 6, height = 6, dpi = 150)

9. Covariate-Factor correlation

#samples_metadata(MOFAmodel) <- samples_metadata(MOFAobject)
covariates <- samples_metadata(MOFAmodel)[,
  27:ncol(samples_metadata(MOFAmodel))
] %>%
  colnames()
groups <- samples_metadata(MOFAmodel)[, 1:25] %>% colnames()

# remove columns
# covariates <- covariates[!(grepl("Follow_up", covariates))]
Clin_info <- list(
  Clin_score = covariates[1:12], # Clinical score + Symptom score
  Wet_smear = covariates[13:18], # Wet smear
  Fungal_culture = covariates[19:26], # Fungal culture
  confounders = covariates[27:31], # general confounders
  relationship = covariates[32:35], # relationship
  drugs = covariates[36:52], # drugs
  reproductive_info = covariates[53:68] # reproductive info
)

# reproductive info
# quartz(width = 6, height = 6, dpi = 100)

p <- imap(
  Clin_info,
  ~ wrap_elements(
    panel = ~ correlate_factors_with_covariates(
      MOFAmodel,
      covariates = .x,
      plot = "r", # use "log_pval" to plot log p-values
      transpose = T
    )
  )
)
par(mar = c(0, 0, 0, 0), bg = NA)
# p[["reproductive_info"]]
p

$Clin_score


$Wet_smear


$Fungal_culture


$confounders


$relationship


$drugs


$reproductive_info

imap(
  p,
  ~ {
    vars <- Clin_info[[.y]]

    h <- max(3, length(vars) * 0.35) # height per variable
    w <- max(6, max(nchar(vars)) * 0.18) # width per character

    ggsave(
      filename = paste0("../Results/Covar_cor/", "Fact_", .y, ".png"),
      plot = .x,
      width = w,
      height = h,
      dpi = 300
    )
  }
)

$Clin_score
[1] "../Results/Covar_cor/Fact_Clin_score.png"

$Wet_smear
[1] "../Results/Covar_cor/Fact_Wet_smear.png"

$Fungal_culture
[1] "../Results/Covar_cor/Fact_Fungal_culture.png"

$confounders
[1] "../Results/Covar_cor/Fact_confounders.png"

$relationship
[1] "../Results/Covar_cor/Fact_relationship.png"

$drugs
[1] "../Results/Covar_cor/Fact_drugs.png"

$reproductive_info
[1] "../Results/Covar_cor/Fact_reproductive_info.png"

10. Factor visualisation

p <- plot_factors(
  MOFAmodel,
  factors = c(1, 2, 3),
  dot_size = 1,
  color_by = "RVVC_AS"
) +
  scale_fill_manual(
    values = pal$RVVC_AS,
    na.value = "transparent",
    aesthetics = c("colour", "fill")
  )
p

ggsave(
  filename = paste0("../Results/Factor_plots/", "Factor_1-3_grid", "", ".png"),
  plot = p,
  width = 7,
  height = 5,
  dpi = 300
)

Code

plot_factor_set <- function(
  MOFAmodel,
  factors,
  color_vars,
  pal,
  out_file,
  height = 5,
  width_per_plot = 2.8
) {
  plots <- imap(
    color_vars,
    ~ plot_factor(
      MOFAmodel,
      factor = factors,
      color_by = .x,
      dot_size = 4,
      dodge = TRUE,
      stroke = 0.4,
      add_violin = TRUE,
      add_boxplot = FALSE
    ) +
      scale_fill_manual(values = pal[[.x]], na.value = "white") +
      theme(
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank()
      )
  )

  p <- patchwork::wrap_plots(plots, nrow = 1) +
    patchwork::plot_layout(guides = "collect", axis_titles = "collect") &
    theme(legend.position = "bottom")

  ggsave(
    filename = out_file,
    plot = p,
    width = width_per_plot * length(color_vars),
    height = height,
    dpi = 300
  )

  p
}

meta_clus <- colnames(annot_col)

fctors <- c(
  "trx_louvain_7",
  "metab_louvain",
  "microb_louvain",
  "integ_louvain"
) %>%
  set_names()

fctors <- c(
  "RVVC_AS",
  "pos",
  "Clinical_score_(0-5)",
  "RVVC_pos",
  "metab_louvain"
) %>%
  set_names()

map(
  c(1, 2, 3),
  ~ plot_factor_set(
    MOFAmodel = MOFAmodel,
    factors = .x,
    color_vars = fctors,
    pal = pal,
    out_file = paste0("../Results/Factor_plots/Factor", .x, "_gr", ".png")
  )
)

[[1]]


[[2]]


[[3]]

11. Feature-level analysis

views <- c("Transcriptomics", "Metabolomics", "Metagenomics")
p1 <- map(
  views,
  ~ plot_weights(
    MOFAmodel,
    view = .x,
    factor = 1,
    nfeatures = 10,
    text_size = 3
  )
)
p2 <- map(
  views,
  ~ plot_top_weights(
    MOFAmodel,
    factor = 1,
    view = .x,
    nfeatures = 10
  )
)
p <- map2(p1, p2, ~ plot_grid(plotlist = list(.x, .y))) %>% set_names(., views)

Code

Red <- brewer.pal(6, name = "Reds") #%>% c(., "gray")#| fig-height: 5
pal2 <- c("#2266ac", "#91c5de")

meta_headers <- colnames(samples_metadata(MOFAmodel))
gr_df <- samples_metadata(MOFAmodel)[, c(
  "sample",
  "pos",
  #"Clin_gr",
  "BV",
  "RVVC_pos",
  "Wet smear: clue cells (y/n)",
  "metab_louvain"
)] %>% #"sample",
  mutate_at(., 1:ncol(.), factor)
rownames(gr_df) <- gr_df$sample

ann_colors = list(
  metab_louvain = set_names(c(pal$metab_louvain), levels(gr_df$metab_louvain)),
  "Wet smear: clue cells (y/n)" = set_names(
    c(pal$pos),
    levels(gr_df$`Wet smear: clue cells (y/n)`)
  ),
  BV = set_names(c(pal$BV), levels(gr_df$BV)),
  Clin_gr = set_names(c(pal$Clin_gr), levels(gr_df$Clin_gr)),
  pos = set_names(c(pal$pos), levels(gr_df$pos)),
  RVVC_AS = set_names(c(pal$RVVC_AS), levels(gr_df$RVVC_AS)),
  RVVC_pos = set_names(c(pal$RVVC_pos), levels(gr_df$RVVC_pos)),
  WS_lacto = set_names(
    c(pal$pos),
    levels(gr_df$`Wet smear: amount of lactobacilli (normal/decreased)`)
  )
)

plot_data_heatmap(
  MOFAmodel,
  view = "Transcriptomics",
  factor = 1,
  features = 20,
  annotation_samples = gr_df[, -1], #gr_df[,-1], "Clin_gr"
  denoise = TRUE,
  scale = "row",
  annotation_colors = ann_colors
)

'annotation_samples' provided as a data.frame, please make sure that the rownames match the sample names

Code

plot_data_heatmap(
  MOFAmodel,
  view = "Metabolomics",
  factor = 4,
  features = 20,
  annotation_samples = "metab_louvain", #gr_df[,-1], "Clin_gr"
  denoise = TRUE,
  scale = "row",
  annotation_colors = ann_colors
)

plot_data_heatmap has an interesting argument to “beautify” the heatmap: denoise = TRUE. Instead of plotting the (noisy) input data, we can plot the data reconstructed by the model, where noise has been removed:

p <- plot_factors(
  MOFAmodel,
  factors = c(1, 2),
  color_by = "Clinical_score_(0-5)",
  shape_by = "pos",
  dot_size = 2.5,

  show_missing = T
) +
  scale_fill_manual(values = pal$`Clinical_score_(0-5)`, na.value = "white")

p <- p +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_vline(xintercept = (-0), linetype = "dashed")

p

plot_data_scatter(
  MOFAmodel,
  view = "Transcriptomics",
  factor = 1,
  features = 20,
  color_by = "RVVC_AS"
) +
  scale_fill_manual(values = pal$RVVC_AS, na.value = "transparent")

plot_data_scatter(
  MOFAmodel,
  view = "Metabolomics",
  factor = 1,
  features = 20,
  color_by = "RVVC_AS"
) +
  scale_fill_manual(values = pal$Clin_gr, na.value = "transparent")

12. Dimensionality reduction

set.seed(42)
MOFAmodel <- run_umap(MOFAmodel)

plot_dimred(
  MOFAmodel,
  method = "UMAP",
  color_by = "RVVC_AS",
  dot_size = 5
) +
  scale_fill_manual(values = pal$Clin_gr, na.value = "transparent")

Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
of ggplot2 3.3.4.
ℹ The deprecated feature was likely used in the MOFA2 package.
  Please report the issue at <https://github.com/bioFAM/MOFA2>.

#MOFAmodel <- run_umap(MOFAmodel, factors = c("Factor4"))

Summary

This file is a fully executable Quarto document with named chunks for reproducibility, caching, and debugging.

# code not currently in use, but could be usefull

## ---- overview-plot ------------------------------------------------
# shows the same as overview plot, but coloured by metadata and not by omic type
mofa_df %>%
  left_join(select(meta, sample, RVVC_AS), by = "sample") %>%
  select(sample, view, RVVC_AS) %>%
  unique() %>%
  mutate(RVVC_AS = factor(.$RVVC_AS)) %>%
  mutate(sample = fct_reorder(sample, as.integer(RVVC_AS), .na_rm = FALSE)) %>%

  ggplot(., aes(x = sample, y = view, fill = RVVC_AS)) +
  geom_tile() + #scale_fill_manual(values = c(missing = "grey", colors)) +
  theme_nothing() +
  theme(
    axis.text.y = element_text(size = 12),
    legend.position = "top",
    plot.margin = unit(c(1, 1, 0, 1), "cm")
  ) +
  scale_fill_manual(values = pal$Clin_bin, na.value = "transparent")


## ---- mofa-variance-barplot ------------------------------------------------
plot_variance_explained(
  MOFAmodel,
  plot_total = FALSE
)

df_long <- get_factors(
  MOFAmodel,
  factors = "all",
  groups = "all",
  as.data.frame = TRUE
) %>%
  left_join(., samples_metadata(MOFAmodel)[, -1], by = "sample") %>%
  as_tibble()

df_wide <- pivot_wider(df_long, names_from = "factor", values_from = "value")
plot_fact.fun <- function(X, Y, gr_val, pal) {
  p <- ggplot(
    df_wide,
    aes(x = .data[[X]], y = .data[[Y]], colour = .data[[gr_val]])
  ) +
    geom_point(size = 2, alpha = 0.9) +
    labs(x = X, y = Y) +
    theme_classic() +
    scale_fill_manual(
      values = pal[1:length(unique(na.omit(df_wide[[gr_val]])))],
      aesthetics = c("colour", "fill")
    ) +
    theme(
      axis.text = element_text(size = rel(0.8), color = "#373844"),
      axis.title = element_text(size = rel(1.1), color = "#373844"),
      axis.line = element_line(color = "#373844", linewidth = 0.5),
      axis.ticks = element_line(color = "#373844", linewidth = 0.5)
    )
  return(p)
}
map(vars, ~ plot_fact.fun("Factor1", "Factor3", .x, pal3))
plot_fact.fun("Factor1", "Factor3", "group_2", pal3)

## other version:
# df_long <- get_factors(
#   MOFAmodel,
#   factors = "all",
#   groups = "all",
#   as.data.frame = TRUE
# ) %>%
#   left_join(., samples_metadata(MOFAobject)[, -2], by = "sample") %>%
#   as_tibble()

# df_wide <- pivot_wider(
#   df_long,
#   names_from = "factor",
#   values_from = "value"
# ) %>%
#   mutate(lab = ifelse(.$RVVC_AS == "RVVC_AS", .$sample, NA))

# plot_fact.fun <- function(X, Y, gr_val, pal) {
#   p <- ggplot(
#     df_wide,
#     aes(x = .data[[X]], y = .data[[Y]], colour = .data[[gr_val]])
#   ) +
#     geom_point(size = 2, alpha = 0.9) +
#     geom_text(
#       aes(label = .data[["lab"]]),
#       size = 3,
#       hjust = 0,
#       nudge_x = 0.07,
#       color = "gray51"
#     ) +
#     labs(x = X, y = Y) +
#     theme_classic() +
#     scale_fill_manual(
#       na.value = "white",
#       values = pal[1:length(unique(na.omit(df_wide[[gr_val]])))],
#       aesthetics = c("colour", "fill")
#     ) +
#     theme(
#       axis.text = element_text(size = rel(0.8), color = "#373844"),
#       axis.title = element_text(size = rel(1.1), color = "#373844"),
#       axis.line = element_line(color = "#373844", linewidth = 0.5),
#       axis.ticks = element_line(color = "#373844", linewidth = 0.5)
#     )
#   return(p)
# }
# vars <- c(
#   "RVVC_AS",
#   "Clin_gr",
#   "group_2",
#   "hyphae",
#   "group_cross",
#   "Clinical_score_(0-5)"
# ) %>%
#   set_names()

# # colour pallets
# blue <- brewer.pal(6, name = "Blues") %>% c(., "gray")
# Red <- brewer.pal(6, name = "Reds") #%>% c(., "gray")
# pal <- c("#f26386", "#fd814e", "#a4d984", "#fbbc52") # "#f588af", "#fd814e"
# pal2 <- c("#2266ac", "#91c5de")

# #Red <- brewer.pal(6, name = "Reds") #%>% c(., "gray")
# pal1 <- c("#ffa998", '#FB5273', "#902267") %>% c(., "gray") #
# pal3 <- c("#9e8dec", "#cc93cf", "#a5c97b", "#de9548") #"#abcb4b" "#63d3b4" "#902267",

# plot_fact.fun("Factor1", "Factor3", "group_2", pal3)

# p <- map(vars, ~ plot_fact.fun("Factor2", "Factor1", .x, pal3))
# p[["RVVC_AS"]] +
#   stat_ellipse(
#     aes(color = RVVC_AS, fill = RVVC_AS),
#     geom = "polygon",
#     alpha = 0.25
#   )

## ---- weights-by-view ------------------------------------------------
# test the
weights_TR <- get_weights(MOFAmodel, views = "Transcriptomics")$Transcriptomics
head(sort(abs(weights_TR[, 1]), decreasing = TRUE))

TRX <- t(X$species$TR)
mt_genes <- grepl("^MT", rownames(TRX))
mt_fraction <- colSums(TRX[mt_genes, ]) / colSums(TRX)

cor(
  get_factors(MOFAmodel)$group1[, 1],
  mt_fraction,
  use = "complete.obs"
)

--- title: "Multi-omics integration with MOFA2" date: today date-format: "D MMM YYYY" format: html: embed-resources: true toc: true toc-depth: 2 highlight-style: github code-tools: source: true toggle: true params: sparse: false --- # Overview This report presents a **structured MOFA2 analysis** integrating transcriptomics, metagenomics, and metabolomics data. The goal is to identify **latent factors capturing shared and view-specific sources of variation**, and to generate a **set of publication-ready figures** that can be combined into a multi‑panel figure. **Main outcomes**: - Latent factor structure across multi‑omics layers - Variance explained per factor and per view - Associations between factors and clinical / technical covariates - Feature loadings driving key factors --- ## Analysis workflow (step‑by‑step) 1. Load required libraries and define plotting palettes 2. Load omics data and sample metadata 3. Format MOFA input 4. Select samples for inclusion 5. Unsupervised UMAPs 6. Configure and run the MOFA model 7. Load trained MOFA model 8. Perform model diagnostics and quality checks 9. Covariates- Factor correlation 10. Annotate latent factors using group information 11. Feature-level figures 12. Unified UMAP (all views) --- ## 1. Libraries and global settings ```{r libraries} #| code-fold: true #| output: false #| warning: false #| message: false library(MOFA2) library(tidyverse) library(ggpubr) library(GGally) library(psych) library(pheatmap) library(cowplot) library(RColorBrewer) library(readxl) library(ComplexHeatmap) library(circlize) library(patchwork) # setwd("/Users/vilkal/work/Brolidens_work/Projects/Candida-omics/src") ``` Define consistent colour palettes to ensure visual coherence across all figures. ```{r colour-pallet} #| code-fold: true select <- dplyr::select map <- purrr::map rename <- dplyr::rename pal <- list( subCST = c( "#a5c97b", "#cc93cf", "#de9548", "#63d3b4", "#9e8dec", "#902267", '#FB5273', "#91c5de" ), RVVC = c("#EAEAEA", "#fd814e"), BV = c("#EAEAEA", "#fd814e"), NAC = c("#EAEAEA", "#fd814e"), ST = c("#EAEAEA", "#fd814e"), missing_RVVC = c("#EAEAEA", "#fd814e"), Clin = c("#a5c97b", "#cc93cf", "#de9548", "#63d3b4", "#9e8dec", "#902267"), Clin_gr = c("#ffa998", '#FB5273', "#902267"), RVVC_AS = c("#ffa998", '#FB5273', "#902267"), RVVC_pos = c( "#A6CEE3", "#1F78B4", #"#EAEAEA", #"gray", "#e490c4ff", "#c12d88ff" ), # "#FB9A99", "#E31A1C", "#B2DF8A", "#609c5bff", "#FDBF6F", "#FF7F00",, "#CAB2D6", "#6a3d9a" "#e490c4ff", "#c12d88ff" trx_louvain_7 = c( "#de9548", "#cc93cf", "#a5c97b", "#9e8dec", "#63d3b4", "#902267", '#FB5273' ), metab_louvain = c( "#a5c97b", "#cc93cf", "#de9548", "#63d3b4", "#9e8dec", "#902267", '#FB5273' ), microb_louvain = c( "#de9548", "#cc93cf", "#a5c97b", "#9e8dec", "#902267", "#63d3b4" ), integ_louvain = c( "#a5c97b", "#cc93cf", "#de9548", "#63d3b4", "#9e8dec", "#902267", '#FB5273', "#984EA3" #"#B3CDE3" ), "Wet_smear:_clue_cells_(y/n)" = c("#2266ac", "#91c5de"), hyphae = c("#f26386", "#fd814e", "#a4d984", "#fbbc52"), pos = c("#91c5de", "#2266ac"), "Clinical_score_(0-5)" = brewer.pal(6, name = "Reds") ) gr_names <- c("Control", "RVVC", "RVVC_H") ``` ## 2. Data loading Multi‑omics matrices and sample metadata are loaded from pre‑processed RDS/CSV files. These data have already undergone quality control and normalisation upstream. ```{r load_data} #| warning: false #| message: false Clin_path <- "/Users/vilkal/work/Brolidens_work/Projects/Candida-omics/data/Metadata_svamp_FINAL.xlsx" comb_meta_path <- "../Results/Combined_gr_meta_data.csv" # meta data Clin_data <- read_xlsx(Clin_path, sheet = "Metadata", na = "na") meta <- read_csv(comb_meta_path) %>% rename(sample = "svamp_ID") %>% mutate(across(2:25, ~ factor(.x))) top_hvg <- read_csv("../Results/Tissue_QC/HVG.csv") TRX_path <- "../Results/MixOmic/Transcriptomics_batch_filt.csv" MG_path <- "../Results/MixOmic/Metageonome_vagswab_CLR_transformed.RDS" MB_path <- "../Results/MixOmic/Metabolom_Normalized.csv" # expression data TRX_batch <- read.csv(TRX_path, row.names = 1) MG_matrix <- readRDS(MG_path) MB_matrix <- read.csv(MB_path, row.names = 1) ``` ## 3. Prepare MOFA input ### 4.1 Assemble omics views MOFA describes each omic dataset as a "view". One of the possible inputs to mofa is a list of matrixes for each view. Each omics layer is formatted as a **features × samples** matrix. Sample ordering needs to be the same. The top 5000 most higly variable genes identified in the `QC_Transcriptomics.qmd` script is used to filter the transcriptomics data. ```{r assemble_views} # remove long strain id, replace with int MG_matrix <- MG_matrix[["clr_data"]][["strain"]] new_names <- sub("\\s+\\S+$", "", colnames(MG_matrix)) # remove strain id new_names <- make.unique(new_names, sep = " ") # Make names unique colnames(MG_matrix) <- new_names # Assign new names # select 5000 most higly variable genes TRX_HVG <- TRX_batch[top_hvg$gene[1:5000], ] views <- list( Transcriptomics = as.matrix(TRX_HVG), Metagenomics = unclass(t(MG_matrix)), Metabolomics = as.matrix(MB_matrix) ) ``` ### 4.2 Convert matrices to long format The matrix list input is only valid if you have matching samples for all "views". If you wish to include samples that are not "complete" in this sense the long format version is another input alternative to create a MOFA model ```{r omics_to_long} omics_to_long <- function(mat, view_name) { mat %>% as.data.frame() %>% rownames_to_column("feature") %>% pivot_longer( cols = -feature, names_to = "sample", values_to = "value" ) %>% mutate(view = view_name) } mofa_df <- imap_dfr(views, ~ omics_to_long(.x, .y)) ``` ### 4.3 Sample criteria df The tibble called `m` shows what samples excist across views for each study participant ```{r identify-samples-overlap} # select samples to include m <- mofa_df %>% distinct(sample, view) %>% mutate(present = TRUE) %>% pivot_wider( id_cols = sample, names_from = view, values_from = present, values_fill = FALSE ) m <- m %>% mutate( n_views = rowSums(across(-sample)), at_least_two = n_views >= 2, all_BL = !(grepl("_3M|_1W|_6M", .$sample)), matched = n_views == 3 ) write_csv(m, "../Results/Sample_omic_table.csv") # m <- write_csv("../Results/Sample_omic_table.csv") knitr::kable(head(m)) ``` Based on the various criteria we filter out the samples we do not wish to include from our long format table ```{r select-samples} mofa_df <- mofa_df %>% filter(sample %in% m$sample[m$at_least_two & m$all_BL]) meta_ <- meta %>% filter(sample %in% m$sample[m$at_least_two & m$all_BL]) ``` ## 4. Sample heatmap overview With our samples selected we can now plot a overviw heatmap showing all our included samples, which omic views they are represented in and how they are distributed by some of our groups of intrest ```{r heatmap-function} #| code-fold: true heatmap_prepp <- function(keep_s, vars, arrange_by = "Clinical_score_(0-5)") { ### ------------------------------------------------------------ ### 1. Select symptom columns and prepare binary clinical data ### ------------------------------------------------------------ # Extract only samples of interest and symptom score columns # Convert TRUE/FALSE columns to numeric 0/1 and clean column names df_bin <- m %>% #filter(sample %in% m$sample[m$at_least_two & m$all_BL]) %>% filter(sample %in% m$sample[keep_s]) %>% left_join( ., select(meta, sample, RVVC_AS, RVVC_pos, "Clinical_score_(0-5)"), by = "sample" ) %>% mutate( RVVC_AS = factor(RVVC_AS, levels = c("RVVC", "Control", "RVVC_AS")) ) %>% mutate( RVVC_pos = factor( RVVC_pos, levels = c( "RVVC_pos", "RVVC_neg", "Control_pos", "Control_neg" #"NA_pos", #"NA_neg" ) ) ) %>% arrange(desc(n_views)) %>% arrange(RVVC_pos) %>% arrange(desc(`Clinical_score_(0-5)`)) %>% arrange( desc(.data[[arrange_by]]) ) %>% select(1:4) %>% mutate(across(where(is.logical), as.integer)) %>% column_to_rownames("sample") # convert sample ID to rownames # Convert to numeric matrix for ComplexHeatmap mat <- as.matrix(df_bin) # Determine row annotation height scaling factor my_h <<- 0.1 * nrow(mat) ### ------------------------------------------------------------ ### 2. Prepare annotation dataframe (metadata) ### ------------------------------------------------------------ annot_col <- meta %>% filter(sample %in% rownames(df_bin)) %>% # keep same samples as heatmap matrix arrange(match(sample, rownames(df_bin))) %>% select(sample, any_of(vars)) %>% # select annotation variables mutate(across(all_of(vars), ~ factor(.x))) %>% #mutate(across(1:length(vars) + 1, ~ factor(.x))) %>% # ensure annotations are factors column_to_rownames("sample") # convert sample ID to rownames # Quick check: ensure annotation rows match heatmap rows dim(mat) dim(annot_col) setdiff(rownames(mat), rownames(annot_col)) ### ------------------------------------------------------------ ### 4. Define row annotations for heatmap (ComplexHeatmap) ### ------------------------------------------------------------ annot <- HeatmapAnnotation( df = annot_col[, c(vars)], which = c("row"), #which = c("column"), # Clin_gr = annot_col$Clin_gr, # binned clinical grade # "Clinical_score_(0-5)" = annot_col$`Clinical_score_(0-5)`, # raw clinical score # RVVC_AS = annot_col$RVVC_AS, # sample group # BV = annot_col$BV, # sample group # RVVC = annot_col$RVVC, # sample group # hyphae = annot_col$hyphae, # sample group show_annotation_name = TRUE, # show labels annotation_name_gp = gpar(fontsize = 8), # label font size # Legend formatting annotation_legend_param = list( grid_height = unit(.1, "mm"), grid_width = unit(2, "mm"), title = "", labels_gp = gpar(fontsize = 7), title_gp = gpar(fontsize = 8) ), simple_anno_size = unit(.3, "cm"), # annotation row height # Colors for each annotation variable na_col = "white", col = pal[vars] %>% imap(., ~ set_names(.x, levels(annot_col[[.y]]))) ) ### ------------------------------------------------------------ ### Create heatmap ### ------------------------------------------------------------ H <- Heatmap( mat, width = unit(ncol(mat) * 5, "mm"), height = unit(nrow(mat) * 2.2, "mm"), name = "Symptom Present", col = col_fun, # vertical orientation: right_annotation = annot, cluster_rows = FALSE, row_order = rownames(mat), row_names_side = "right", column_names_rot = 90, row_names_gp = gpar(fontsize = 7), heatmap_legend_param = list( at = c(0, 1), labels = c("No", "Yes") ), # cell_fun = function(j, i, x, y, width, height, fill) { # grid.rect(x, y, width, height, gp = gpar(col = "grey70", lwd = 0.5, fill = NA)) # } layer_fun = function(j, i, x, y, width, height, fill) { # Draw cell borders without overwriting fill grid.rect( x = x, y = y, width = width, height = height, gp = gpar(col = "grey70", lwd = 0.4, fill = NA) ) } ) #return(list(mat = mat, annot = annot, h = my_h)) return(H) } ``` ```{r Sample-info-heatmap} #| code-fold: show ### ------------------------------------------------------------ ### Color function for the symptom heatmap ### ------------------------------------------------------------ col_fun <- colorRamp2( c(0, 0.6, 1), c("white", "white", "#e05e85") ) ### ------------------------------------------------------------ ### Prepare annotation df and matrix ### ------------------------------------------------------------ vars <- c( "Clinical_score_(0-5)", "Clin_gr", "RVVC", #"RVVC_AS", "RVVC_pos", "BV", "NAC", "missing_RVVC", "ST" ) keep_s <- rep(TRUE, nrow(m)) # 152 every samples incl. follow up keep_s <- m$all_BL # 91 samples keep_s <- m$matched # 70 samples keep_s <- m$at_least_two & m$all_BL # 89 samples H <- heatmap_prepp(keep_s, vars) H <- heatmap_prepp( keep_s, c("RVVC_pos", "RVVC_AS", "Clinical_score_(0-5)"), arrange_by = "RVVC_pos" ) H <- heatmap_prepp( keep_s, c("pos", "RVVC_pos", "Clinical_score_(0-5)"), arrange_by = "RVVC_AS" ) # quartz(width = 6, height = 6, dpi = 150) # draw(H) ``` ```{r save-plot} #| code-fold: true #| output: true #| fig-width: 8.5 #| fig-height: 11 # !expr my_h # Heatmap global options: # ht_opt$COLUMN_ANNO_PADDING = unit(.05, "cm") # ht_opt$HEATMAP_LEGEND_PADDING = unit(-0, "cm") #unit(c(0, 0, 0, -1), "cm") #b,l,t,r # ht_opt$TITLE_PADDING = unit(.05, "cm") # ht_opt$DIMNAME_PADDING = unit(.05, "cm") # ht_opt(LEGEND_GAP = unit(18, "mm")) H_g <- draw( H, heatmap_legend_side = "bottom", merge_legend = F, background = NA, show_annotation_legend = FALSE, legend_gap = unit(5, "mm"), padding = unit(c(3, 9, -2, 3), "mm") # outer padding ) # b,l,t,r ################ # SAVE RESULTS # ################ # quartz(width = 6, height = 6, dpi = 150) png( file = paste0("../Results/Sample_HT/BL_89.png"), units = "in", res = 600, #width = 8.5, height = 10 # Epi width = 8.5, height = 11 # 11 # my_h # 17 (all samples) ) H_g #draw(H) dev.off() ``` ## 5. Unsupervised UMAPs We plot unsupervised umaps coloured by different groups of intrest in order to see how they cluster across views. !NB should think about doing with and without HVGs only ```{r filter-samples-for-UMAP} # filter samples from views for UMAP ploting m_ <- m %>% filter(sample %in% .$sample[m$at_least_two & m$all_BL]) v <- views %>% imap(., ~ .x[, m_$sample[m_[[.y]]]]) ``` This first code is taken from Paulos analysis of the metabolomics data. It gives more control over the knn clustering by perfoming it in a seperate step and then supplying the knn to the umap function. The other alternative bellow is just supplying the data to umap, which then runs the PCA and knn internaly with default setings. ```{r Paulos-UMAP} #| eval: false #| code-fold: true #### UMAP analysis #### set.seed(1) UMAP_df <- v %>% map(., ~ t(.x)) %>% # PCA map(., ~ prcomp(.x, scale = T, center = T)) %>% # KNN graph map( ., ~ RcppHNSW::hnsw_knn(as.matrix(.x$x[, 1:10]), k = 10, distance = "cosine") ) %>% # UMAP map( ., ~ uwot::umap( X = NULL, nn_method = .x, n_components = 2, ret_extra = c("model", "fgraph"), verbose = T, min_dist = 0.1, spread = 0.3, approx_pow = TRUE, repulsion_strength = .2, negative_sample_rate = 30, n_epochs = 200, n_threads = 8 )$embedding ) %>% imap( ., ~ tibble( "UMAP 1" = .x[, 1], "UMAP 2" = .x[, 2], "ID" = colnames(v[[.y]]) ) ) %>% map(., ~ left_join(., annot_col, by = c("ID" = "sample"))) ``` ```{r UMAP-function} #| code-fold: true UMAP_fun <- function( umap_df, group_var, txt_var = NULL, shape_gr = NULL, shape = NULL, title = "" ) { if (is.null(shape_gr)) { point <- geom_point(size = 3, alpha = 1, shape = 21) } else { point <- geom_point( size = 3, alpha = 1, aes(shape = if (!is.null(shape_gr)) .data[[shape_gr]] else NULL) ) } p <- ggplot( umap_df, aes( x = `UMAP 1`, y = `UMAP 2`, #shape = RVVC_AS, fill = .data[[group_var]], color = .data[[group_var]] ) ) + point + #geom_jitter( shape=21, size=3, color="white", width=.5, height=.5) + # Tassos used jitter scale_fill_manual( values = pal[[group_var]], na.value = "transparent", name = "" #aesthetics = c("colour", "fill") ) + guides(fill = "none") + scale_colour_manual( values = pal[[group_var]], na.value = "gray", name = "" ) + { if (!is.null(shape_gr) && !is.null(shape)) { scale_shape_manual(values = shape, name = "") } } + # scale_shape_manual(values = c(21, 5, 4), name = "" ) + #specify individual shapes+ { if (!is.null(txt_var)) { geom_text( data = umap_df, aes(x = `UMAP 1`, y = `UMAP 2`, label = .data[[txt_var]]), size = 2, hjust = 0, nudge_x = 0.07, color = "gray51" ) } } + ggtitle(title) + theme_bw(base_size = 14) + theme( # point = element_point(shape = 21), line = element_blank(), axis.ticks = element_line(color = "black"), aspect.ratio = 1, axis.title = element_blank() ) return(p) } #### UMAP plotting by groups #### # save plot to file #ggsave(paste0("./Figures/09/", "Bulk_UMAP.png"), p) ``` ```{r plot-UMAP} #| fig-width: 14 #| fig-height: 5 #| code-fold: true #| warning: false #### group information #### annot_col <- meta %>% #filter(sample %in% .$sample[m$at_least_two & m$all_BL]) %>% # select(-`Age (years)`) %>% select( 1:22, `Clinical_score_(0-5)` ) %>% mutate(across(2:ncol(.), ~ factor(.x))) %>% mutate( txt_clin = ifelse( grepl("0", .$`Clinical_score_(0-5)`), NA, as.character(.$`Clinical_score_(0-5)`) ) ) %>% mutate(txt_id = ifelse(RVVC == "RVVC", .$sample, NA)) #### UMAP analysis #### set.seed(1) umap_dfs <- v %>% map(., ~ t(.x)) %>% map( ., ~ uwot::umap( .x, #seed = 123, seed = 463, n_neighbors = 15, #metric = "cosine", init = "spectral", scale = T ) ) %>% map( ., ~ tibble( "UMAP 1" = .x[, 1], "UMAP 2" = .x[, 2], "ID" = rownames(.x) ) ) %>% map(., ~ left_join(., annot_col, by = c("ID" = "sample"))) meta_clus <- colnames(annot_col) color_vars <- c( "pos", "trx_louvain_7", "metab_louvain", "microb_louvain", "integ_louvain", "Clinical_score_(0-5)", "RVVC_pos", "RVVC_AS" ) %>% set_names(.) #### Combine and save plots #### make_umap_panel <- function(var, umap_dfs, outdir = "../Results/UMAP/") { # create plots for each dataset p_list <- imap( umap_dfs, ~ UMAP_fun(.x, var, "txt_clin", title = .y) ) # combine with shared legend p_combined <- patchwork::wrap_plots(p_list) + patchwork::plot_layout(guides = "collect") & theme(legend.position = "right") # safe filename fname <- gsub("[^A-Za-z0-9_]", "_", var) # save ggsave( filename = paste0(outdir, "UMAP_", fname, ".png"), plot = p_combined, width = 14, height = 5, dpi = 300 ) return(p_combined) } plots <- map(color_vars, ~ make_umap_panel(.x, umap_dfs)) plots # use shapes: # UMAP_fun(umap_dfs$Metabolomics, "trx_louvain_7", "txt_clin", shape_gr = "pos", shape = c(21, 24)) # no shapes # UMAP_fun(umap_dfs$Metabolomics, "trx_louvain_7", "BV") ``` ## 6. MOFA model construction A MOFA object is created and configured with default data and training options. The number of latent factors is set a priori and can be tuned depending on variance explained. ### Data options <details> <summary> Important arguments</summary> - **`scale_groups`** Scale groups to the same total variance? *Default:* `FALSE` - **`scale_views`** Scale views to the same total variance? *Default:* `FALSE` - **`views`** Names of the views to include in the model. - **`groups`** Names of the sample groups. </details> ### Model options <details> <summary> Important arguments</summary> - **`num_factors`** Number of latent factors. - **`likelihoods`** Likelihood per view. Options: `"gaussian"`, `"poisson"`, `"bernoulli"` By default, likelihoods are inferred automatically. - **`spikeslab_factors`** Use spike-and-slab sparsity prior on the factors? *Default:* `FALSE` - **`spikeslab_weights`** Use spike-and-slab sparsity prior on the weights? *Default:* `TRUE` - **`ard_factors`** Use Automatic Relevance Determination (ARD) prior on the factors? *Default:* `TRUE` when using multiple groups. - **`ard_weights`** Use Automatic Relevance Determination (ARD) prior on the weights? *Default:* `TRUE` when using multiple views. </details> ### Training options <details> <summary> Important arguments</summary> - **`maxiter`** Maximum number of training iterations. *Default:* `1000` - **`convergence_mode`** Convergence speed of the algorithm. Options: `"fast"`, `"medium"` (default), `"slow"` For exploratory analyses, `"fast"` is usually sufficient. - **`seed`** Random seed for reproducibility. </details> We use default options, however it is recomended to run the model several times with different seeds. In one of the papers published by MOFAS authors, they ran the model ~24 times, before picking the model with the highest elbo score. ```{r mofa_setup} MOFAobject <- create_mofa_from_df(mofa_df) data_opts <- get_default_data_options(MOFAobject) model_opts <- get_default_model_options(MOFAobject) train_opts <- get_default_training_options(MOFAobject) #model_opts$num_factors <- 10 train_opts$convergence_mode <- "medium" train_opts$seed <- 1234 ``` The functions bellow runs the model with different seeds and saves the model and a table with all seeds and corresponding elbo scores as output ```{r train-mofa-model} #| code-fold: true #| eval: false #| output: false #| code-summary: "Train MOFA model" #| warning: false #| message: false seeds <- c(3, 100, 50) run_mofa_with_seed <- function(seed) { # Make a local copy (important!) train_opts$seed <- seed MOFAobject <- prepare_mofa( MOFAobject, data_options = data_opts, model_options = model_opts, training_options = train_opts ) MOFAmodel <- run_mofa( MOFAobject, outfile = paste0("../Results/MOFA/MOFA_allBL_seed_", seed, ".hdf5"), use_basilisk = TRUE ) elbo <- max(get_elbo(MOFAmodel)) # Return a single-row data frame tibble( seed = seed, elbo = elbo ) } # existing training code stays the same elbo_table <- map_dfr(seeds, ~ run_mofa_with_seed(.x)) write_csv(elbo_table, "../Results/MOFA/elbo_table.csv") best_seed <- elbo_table$seed[which.max(elbo_table$elbo)] ``` MOFAobject without transcriptomics batch correction ```{r model-noBatch} #| eval: false X <- readRDS("../Results/MixOmic/X_object.RDS") views <- list( Transcriptomics = t(X$strain$TR), Metagenomics = t(X$strain$MG), Metabolomics = t(X$species$MB) ) MOFAobject <- create_mofa(views) data_opts <- get_default_data_options(MOFAobject) model_opts <- get_default_model_options(MOFAobject) model_opts$num_factors <- 10 train_opts <- get_default_training_options(MOFAobject) train_opts$convergence_mode <- "medium" train_opts$seed <- 1234 MOFAobject <- prepare_mofa( MOFAobject, data_options = data_opts, model_options = model_opts, training_options = train_opts ) MOFAmodel <- run_mofa( MOFAobject, outfile = "../Results/MOFA/MOFA_noBatch_strain.hdf5", use_basilisk = TRUE ) ``` ## 7. Load trained MOFA model We load the model with the highest elbo score ```{r load_model} #| eval: true #| include: true #| warning: false #| message: false #| tbl-colwidths: [60,40] elbo_table <- read_csv("../Results/MOFA/elbo_table.csv") knitr::kable(elbo_table, ) best_seed <- elbo_table$seed[which.max(abs(elbo_table$elbo))] MOFAmodel <- load_model( #"../Results/MOFA/MOFA_matched_seed_50.hdf5" paste0("../Results/MOFA/MOFA_allBL_seed_", best_seed, ".hdf5") ) # existing training code stays the same samples_metadata(MOFAmodel) <- samples_metadata(MOFAmodel)[, c( "sample", "group" )] %>% left_join(meta, by = "sample") # batch corrected tr, ~17 000 genes, strain lvl, no followup samples "../Results/MOFA/MOFA_matched_noM3_strain.hdf5" # batch corrected tr, ~17 000 genes, strain lvl + 7 M3 follow up samples "../Results/MOFA/MOFA_w73M_strain.hdf5" # same as above + 9 extra RVVC samples without matching trx "../Results/MOFA/MOFA_w9RVVC_strain.hdf5" # same as above + all 154 samples "../Results/MOFA/MOFA_all_154_samples_strain.hdf5" ``` ## 8. Model diagnostics ```{r view-distribution} #| fig-width: 8 #| fig-height: 6 # inspect data distribution ggdensity(mofa_df, x = "value", fill = "gray70") + facet_wrap(~view, nrow = 3, scales = "free") # + xlim(0.5,) ``` We first assess the overall quality of the model by inspecting variance explained and factor correlations. ```{r model-overview} #| fig-width: 8 #| fig-height: 2 # inspect the model: plot_data_overview(MOFAmodel) MOFAmodel ``` ```{r variance_explained} #| warning: false #| message: false #| fig-width: 9.5 #| fig-height: 2.7 p1 <- plot_variance_explained(MOFAmodel, plot_total = TRUE)[[2]] p2 <- plot_variance_explained(MOFAmodel, factors = 1:10) #+ #+ #theme(axis.text.x = element_text(angle = 45, hjust = 0.8, vjust = 0.9)) # plot_grid(plotlist = list(p1, p2), ncol = 2) p <- plot_data_overview(MOFAmodel) + theme(plot.margin = unit(c(1, 0, 1, 1), "cm")) p1 <- p1 + coord_flip() + theme_nothing() + theme(plot.margin = unit(c(1, 0.1, 1, 0), "cm")) p2 <- p2 + coord_flip() + theme(axis.text.y = element_blank(), legend.position = "none") + theme(plot.margin = unit(c(1, 0.5, 1, 0), "cm")) + scale_y_discrete(labels = 1:10) # quartz(width = 9.5, height = 2.7) plot_grid(plotlist = list(p, p2, p1), ncol = 3, align = c("h")) # p$layers[[1]]$aes_params$alpha ``` #### Correlation between factors A good sanity check is to verify that the Factors are largely uncorrelated. In MOFA there are no orthogonality constraints such as in Principal Component Analysis, but if there is a lot of correlation between Factors this suggests a poor model fit. Reasons could be that you used too many factors or perhaps the normalisation is not adequate. ```{r factor_correlations} plot_factor_cor(MOFAmodel) ``` ```{r factors_by_batch} MOFAbatch <- load_model( "../Results/MOFA/MOFA_noBatch_strain.hdf5" ) samples_metadata(MOFAbatch) <- samples_metadata(MOFAbatch) %>% left_join(meta, by = "sample") plot_factors( MOFAbatch, factors = "all", color_by = "batch" ) ``` When running MOFA the first time, the internal quality control warned that at least one of the factors was strongly corelated with the total number of expressed features in one of the views. This is typically a result of batch effect. As seen in the plot above Factor 1 perfectly seperates the two batches of the transcriptomics data. Thus batch correction was performe in the upstream `QC_Transcriptomics.qmd` script. ```{r factors_by_clinical} #| fig-width: 10 #| fig-height: 5 p <- plot_factors( MOFAmodel, factors = "all", color_by = "Clinical_score_(0-5)" ) + scale_fill_manual(values = pal$`Clinical_score_(0-5)`, na.value = "white") p plot_factors( MOFAmodel, factors = c(1, 3), color_by = "Clin_gr" ) + stat_ellipse( #aes(color = RVVC_AS, fill = RVVC_AS), geom = "polygon", alpha = 0.25 ) + scale_fill_manual(values = pal$Clin_gr, na.value = "white") # quartz(width = 6, height = 6, dpi = 150) ``` ## 9. Covariate-Factor correlation ```{r covariates-factor-correlation} #| warning: false #| message: false #samples_metadata(MOFAmodel) <- samples_metadata(MOFAobject) covariates <- samples_metadata(MOFAmodel)[, 27:ncol(samples_metadata(MOFAmodel)) ] %>% colnames() groups <- samples_metadata(MOFAmodel)[, 1:25] %>% colnames() # remove columns # covariates <- covariates[!(grepl("Follow_up", covariates))] Clin_info <- list( Clin_score = covariates[1:12], # Clinical score + Symptom score Wet_smear = covariates[13:18], # Wet smear Fungal_culture = covariates[19:26], # Fungal culture confounders = covariates[27:31], # general confounders relationship = covariates[32:35], # relationship drugs = covariates[36:52], # drugs reproductive_info = covariates[53:68] # reproductive info ) # reproductive info # quartz(width = 6, height = 6, dpi = 100) p <- imap( Clin_info, ~ wrap_elements( panel = ~ correlate_factors_with_covariates( MOFAmodel, covariates = .x, plot = "r", # use "log_pval" to plot log p-values transpose = T ) ) ) par(mar = c(0, 0, 0, 0), bg = NA) # p[["reproductive_info"]] p imap( p, ~ { vars <- Clin_info[[.y]] h <- max(3, length(vars) * 0.35) # height per variable w <- max(6, max(nchar(vars)) * 0.18) # width per character ggsave( filename = paste0("../Results/Covar_cor/", "Fact_", .y, ".png"), plot = .x, width = w, height = h, dpi = 300 ) } ) ``` ## 10. Factor visualisation ```{r Factor-grid-dist-plot} #| fig-width: 7 #| fig-height: 5 p <- plot_factors( MOFAmodel, factors = c(1, 2, 3), dot_size = 1, color_by = "RVVC_AS" ) + scale_fill_manual( values = pal$RVVC_AS, na.value = "transparent", aesthetics = c("colour", "fill") ) p ggsave( filename = paste0("../Results/Factor_plots/", "Factor_1-3_grid", "", ".png"), plot = p, width = 7, height = 5, dpi = 300 ) ``` ```{r Factor-violin-plots} #| code-fold: true #| warning: false #| message: false #| fig-width: 10 #| fig-height: 5 plot_factor_set <- function( MOFAmodel, factors, color_vars, pal, out_file, height = 5, width_per_plot = 2.8 ) { plots <- imap( color_vars, ~ plot_factor( MOFAmodel, factor = factors, color_by = .x, dot_size = 4, dodge = TRUE, stroke = 0.4, add_violin = TRUE, add_boxplot = FALSE ) + scale_fill_manual(values = pal[[.x]], na.value = "white") + theme( axis.text.x = element_blank(), axis.ticks.x = element_blank() ) ) p <- patchwork::wrap_plots(plots, nrow = 1) + patchwork::plot_layout(guides = "collect", axis_titles = "collect") & theme(legend.position = "bottom") ggsave( filename = out_file, plot = p, width = width_per_plot * length(color_vars), height = height, dpi = 300 ) p } meta_clus <- colnames(annot_col) fctors <- c( "trx_louvain_7", "metab_louvain", "microb_louvain", "integ_louvain" ) %>% set_names() fctors <- c( "RVVC_AS", "pos", "Clinical_score_(0-5)", "RVVC_pos", "metab_louvain" ) %>% set_names() map( c(1, 2, 3), ~ plot_factor_set( MOFAmodel = MOFAmodel, factors = .x, color_vars = fctors, pal = pal, out_file = paste0("../Results/Factor_plots/Factor", .x, "_gr", ".png") ) ) ``` ## 11. Feature-level analysis ```{r top_weights} #| fig-cap: "Top metagenomic features driving Factor 2" #| fig-width: 10 #| fig-height: 5 views <- c("Transcriptomics", "Metabolomics", "Metagenomics") p1 <- map( views, ~ plot_weights( MOFAmodel, view = .x, factor = 1, nfeatures = 10, text_size = 3 ) ) p2 <- map( views, ~ plot_top_weights( MOFAmodel, factor = 1, view = .x, nfeatures = 10 ) ) p <- map2(p1, p2, ~ plot_grid(plotlist = list(.x, .y))) %>% set_names(., views) ``` ```{r transcriptomics_heatmap} #| fig-width: 10 #| code-fold: true Red <- brewer.pal(6, name = "Reds") #%>% c(., "gray")#| fig-height: 5 pal2 <- c("#2266ac", "#91c5de") meta_headers <- colnames(samples_metadata(MOFAmodel)) gr_df <- samples_metadata(MOFAmodel)[, c( "sample", "pos", #"Clin_gr", "BV", "RVVC_pos", "Wet smear: clue cells (y/n)", "metab_louvain" )] %>% #"sample", mutate_at(., 1:ncol(.), factor) rownames(gr_df) <- gr_df$sample ann_colors = list( metab_louvain = set_names(c(pal$metab_louvain), levels(gr_df$metab_louvain)), "Wet smear: clue cells (y/n)" = set_names( c(pal$pos), levels(gr_df$`Wet smear: clue cells (y/n)`) ), BV = set_names(c(pal$BV), levels(gr_df$BV)), Clin_gr = set_names(c(pal$Clin_gr), levels(gr_df$Clin_gr)), pos = set_names(c(pal$pos), levels(gr_df$pos)), RVVC_AS = set_names(c(pal$RVVC_AS), levels(gr_df$RVVC_AS)), RVVC_pos = set_names(c(pal$RVVC_pos), levels(gr_df$RVVC_pos)), WS_lacto = set_names( c(pal$pos), levels(gr_df$`Wet smear: amount of lactobacilli (normal/decreased)`) ) ) plot_data_heatmap( MOFAmodel, view = "Transcriptomics", factor = 1, features = 20, annotation_samples = gr_df[, -1], #gr_df[,-1], "Clin_gr" denoise = TRUE, scale = "row", annotation_colors = ann_colors ) plot_data_heatmap( MOFAmodel, view = "Metabolomics", factor = 4, features = 20, annotation_samples = "metab_louvain", #gr_df[,-1], "Clin_gr" denoise = TRUE, scale = "row", annotation_colors = ann_colors ) ``` plot_data_heatmap has an interesting argument to “beautify” the heatmap: `denoise = TRUE`. Instead of plotting the (noisy) input data, we can plot the data reconstructed by the model, where noise has been removed: ```{r divide-groups-data} p <- plot_factors( MOFAmodel, factors = c(1, 2), color_by = "Clinical_score_(0-5)", shape_by = "pos", dot_size = 2.5, show_missing = T ) + scale_fill_manual(values = pal$`Clinical_score_(0-5)`, na.value = "white") p <- p + geom_hline(yintercept = 0, linetype = "dashed") + geom_vline(xintercept = (-0), linetype = "dashed") p ``` ```{r scatter-plot} #| fig-width: 13 #| fig-height: 10 plot_data_scatter( MOFAmodel, view = "Transcriptomics", factor = 1, features = 20, color_by = "RVVC_AS" ) + scale_fill_manual(values = pal$RVVC_AS, na.value = "transparent") plot_data_scatter( MOFAmodel, view = "Metabolomics", factor = 1, features = 20, color_by = "RVVC_AS" ) + scale_fill_manual(values = pal$Clin_gr, na.value = "transparent") ``` ## 12. Dimensionality reduction ```{r umap} set.seed(42) MOFAmodel <- run_umap(MOFAmodel) plot_dimred( MOFAmodel, method = "UMAP", color_by = "RVVC_AS", dot_size = 5 ) + scale_fill_manual(values = pal$Clin_gr, na.value = "transparent") #MOFAmodel <- run_umap(MOFAmodel, factors = c("Factor4")) ``` # Summary This file is a fully executable Quarto document with named chunks for reproducibility, caching, and debugging. ```{r} #| eval: false # code not currently in use, but could be usefull ## ---- overview-plot ------------------------------------------------ # shows the same as overview plot, but coloured by metadata and not by omic type mofa_df %>% left_join(select(meta, sample, RVVC_AS), by = "sample") %>% select(sample, view, RVVC_AS) %>% unique() %>% mutate(RVVC_AS = factor(.$RVVC_AS)) %>% mutate(sample = fct_reorder(sample, as.integer(RVVC_AS), .na_rm = FALSE)) %>% ggplot(., aes(x = sample, y = view, fill = RVVC_AS)) + geom_tile() + #scale_fill_manual(values = c(missing = "grey", colors)) + theme_nothing() + theme( axis.text.y = element_text(size = 12), legend.position = "top", plot.margin = unit(c(1, 1, 0, 1), "cm") ) + scale_fill_manual(values = pal$Clin_bin, na.value = "transparent") ## ---- mofa-variance-barplot ------------------------------------------------ plot_variance_explained( MOFAmodel, plot_total = FALSE ) df_long <- get_factors( MOFAmodel, factors = "all", groups = "all", as.data.frame = TRUE ) %>% left_join(., samples_metadata(MOFAmodel)[, -1], by = "sample") %>% as_tibble() df_wide <- pivot_wider(df_long, names_from = "factor", values_from = "value") plot_fact.fun <- function(X, Y, gr_val, pal) { p <- ggplot( df_wide, aes(x = .data[[X]], y = .data[[Y]], colour = .data[[gr_val]]) ) + geom_point(size = 2, alpha = 0.9) + labs(x = X, y = Y) + theme_classic() + scale_fill_manual( values = pal[1:length(unique(na.omit(df_wide[[gr_val]])))], aesthetics = c("colour", "fill") ) + theme( axis.text = element_text(size = rel(0.8), color = "#373844"), axis.title = element_text(size = rel(1.1), color = "#373844"), axis.line = element_line(color = "#373844", linewidth = 0.5), axis.ticks = element_line(color = "#373844", linewidth = 0.5) ) return(p) } map(vars, ~ plot_fact.fun("Factor1", "Factor3", .x, pal3)) plot_fact.fun("Factor1", "Factor3", "group_2", pal3) ## other version: # df_long <- get_factors( # MOFAmodel, # factors = "all", # groups = "all", # as.data.frame = TRUE # ) %>% # left_join(., samples_metadata(MOFAobject)[, -2], by = "sample") %>% # as_tibble() # df_wide <- pivot_wider( # df_long, # names_from = "factor", # values_from = "value" # ) %>% # mutate(lab = ifelse(.$RVVC_AS == "RVVC_AS", .$sample, NA)) # plot_fact.fun <- function(X, Y, gr_val, pal) { # p <- ggplot( # df_wide, # aes(x = .data[[X]], y = .data[[Y]], colour = .data[[gr_val]]) # ) + # geom_point(size = 2, alpha = 0.9) + # geom_text( # aes(label = .data[["lab"]]), # size = 3, # hjust = 0, # nudge_x = 0.07, # color = "gray51" # ) + # labs(x = X, y = Y) + # theme_classic() + # scale_fill_manual( # na.value = "white", # values = pal[1:length(unique(na.omit(df_wide[[gr_val]])))], # aesthetics = c("colour", "fill") # ) + # theme( # axis.text = element_text(size = rel(0.8), color = "#373844"), # axis.title = element_text(size = rel(1.1), color = "#373844"), # axis.line = element_line(color = "#373844", linewidth = 0.5), # axis.ticks = element_line(color = "#373844", linewidth = 0.5) # ) # return(p) # } # vars <- c( # "RVVC_AS", # "Clin_gr", # "group_2", # "hyphae", # "group_cross", # "Clinical_score_(0-5)" # ) %>% # set_names() # # colour pallets # blue <- brewer.pal(6, name = "Blues") %>% c(., "gray") # Red <- brewer.pal(6, name = "Reds") #%>% c(., "gray") # pal <- c("#f26386", "#fd814e", "#a4d984", "#fbbc52") # "#f588af", "#fd814e" # pal2 <- c("#2266ac", "#91c5de") # #Red <- brewer.pal(6, name = "Reds") #%>% c(., "gray") # pal1 <- c("#ffa998", '#FB5273', "#902267") %>% c(., "gray") # # pal3 <- c("#9e8dec", "#cc93cf", "#a5c97b", "#de9548") #"#abcb4b" "#63d3b4" "#902267", # plot_fact.fun("Factor1", "Factor3", "group_2", pal3) # p <- map(vars, ~ plot_fact.fun("Factor2", "Factor1", .x, pal3)) # p[["RVVC_AS"]] + # stat_ellipse( # aes(color = RVVC_AS, fill = RVVC_AS), # geom = "polygon", # alpha = 0.25 # ) ## ---- weights-by-view ------------------------------------------------ # test the weights_TR <- get_weights(MOFAmodel, views = "Transcriptomics")$Transcriptomics head(sort(abs(weights_TR[, 1]), decreasing = TRUE)) TRX <- t(X$species$TR) mt_genes <- grepl("^MT", rownames(TRX)) mt_fraction <- colSums(TRX[mt_genes, ]) / colSums(TRX) cor( get_factors(MOFAmodel)$group1[, 1], mt_fraction, use = "complete.obs" ) ```